Filters








8,255 Hits in 9.6 sec

A Coupled Hidden Markov Random Field model for simultaneous face clustering and tracking in videos

Baoyuan Wu, Bao-Gang Hu, Qiang Ji
2017 Pattern Recognition  
A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos, Pattern Recognition, http://dx.Abstract Face clustering and face tracking are two areas of active research  ...  To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions.  ...  We introduce a Coupled Hidden Markov Random Field (CHMRF) model by coupling two Hidden Markov Random Field (HMRF) models [7] .  ... 
doi:10.1016/j.patcog.2016.10.022 fatcat:xlplaf4dvrcchlgcfjf7opljci

CRF-Based Context Modeling for Person Identification in Broadcast Videos

Paul Gay, Sylvain Meignier, Paul Deléglise, Jean-Marc Odobez
2016 Frontiers in ICT  
videos, conditional random field, face clustering, speaker diarization 15 16 19 the appearances of their different actors.  ...  The proposed approach combines 6 iteratively two Conditional Random Fields (CRF).  ...  audio and video clustering and then associating the clusters to 330 obtain the potential AV person labels P (audio and face cluster couples).  ... 
doi:10.3389/fict.2016.00009 fatcat:emrygzd3uffxtfvu2unctmsw54

Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning [article]

Jingwen Wang, Wenhao Jiang, Lin Ma, Wei Liu, Yong Xu
2018 arXiv   pre-print
Dense video captioning is a newly emerging task that aims at both localizing and describing all events in a video.  ...  By coupling proposal and captioning modules into one unified framework, our model outperforms the state-of-the-arts on the ActivityNet Captions dataset with a relative gain of over 100% (Meteor score increases  ...  Training Our complete dense video captioning model, as illustrated in Fig. 2 , couples the proposal and captioning module together.  ... 
arXiv:1804.00100v2 fatcat:snnkr2e2fzcqxjghqaafqnsdzq

An Efficient Video to Video Face Recognition using Neural Networks

Wilson S., Lenin Fred
2017 International Journal of Computer Applications  
The face recognition system proposed in this paper comprises of three stages video partitioning, feature extraction and neural network for recognition.  ...  In biometrics video based face recovery is vital and this paper proposes an efficient algorithmic mode which achieves high recovery rate.  ...  [23] introduced an adaptive Hidden Markov Model (HMM) to perform video-based face recognition.  ... 
doi:10.5120/ijca2017914924 fatcat:ulpjl4mrfrdjrn46t5mk5gvtnm

Audio-Visual Event Recognition in Surveillance Video Sequences

M. Cristani, M. Bicego, V. Murino
2007 IEEE transactions on multimedia  
Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage and coupled with an audio BG/FG modelling scheme.  ...  This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone.  ...  For example, for what regards the context of video surveillance, in the approach proposed in [21] , audio and visual patterns are used to train an incrementally structured Hidden Markov Model in order  ... 
doi:10.1109/tmm.2006.886263 fatcat:2mny5pqvevfarb34jfs4gnta4y

Simulation of Gesture Recognition for Physical Impairments Peoples
IJARCCE - Computer and Communication Engineering

Jayashree. R, Dinesh. L
2015 IJARCCE  
It focuses in the field include emotion recognition from the face and hand gesture recognition.  ...  Hidden Markov models (HMMs) and related models have become standard in statistics, with applications in areas like speech and other signal processing, bioinformatics etc.  ...  In the proposed system, the face and both hands were tracked. 1) Hidden Markov Models (HMM): HMM is a doubly stochastic model and is appropriate for coping with the stochastic properties in gesture recognition  ... 
doi:10.17148/ijarcce.2015.4243 fatcat:sjhqa2pkxfeixa5pdzkbl56ese

A probabilistic graphical model for topic and preference discovery on social media

Lu Liu, Feida Zhu, Lei Zhang, Shiqiang Yang
2012 Neurocomputing  
Through modeling user-document interactions, PTM cannot only discover topics and preferences simultaneously, but also enable them to inform and benefit each other in a unified framework.  ...  However, these data, while rich in content, are usually sparse in textual descriptive information. For example, a video clip is often associated with only a few tags.  ...  Acknowledgments The work was supported by the National Natural Science Foundation of China under Grants 61103065, 61003097 and 60933013.  ... 
doi:10.1016/j.neucom.2011.05.039 fatcat:bj6kowk5uzczzopqxryshplpae

A Novel Facial Expression Intelligent Recognition Method Using Improved Convolutional Neural Network

Min Shi, Lijun Xu, Xiang Chen
2020 IEEE Access  
As the originator of the clustering algorithm, fuzzy C-means clustering(FCM) algorithm has stable performance and good results.  ...  Based on the CNN, this paper introduces FCM to optimize the feature extraction (FE) capability of the model, and proposes a novel FER algorithm using an improved CNN(F-CNN).  ...  to cluster the face patches in the dataset into K clusters.  ... 
doi:10.1109/access.2020.2982286 fatcat:fyuhl27k6zhdlacyocs2aoooaa

Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos [article]

Kha Gia Quach, Ngan Le, Chi Nhan Duong, Ibsa Jalata, Kaushik Roy, Khoa Luu
2022 arXiv   pre-print
GECV dataset is a collection of videos containing crowds of people. Each video is labeled with emotion categories at three levels: individual faces, group of people, and the entire video frame.  ...  In this paper, we propose an effective deep feature level fusion mechanism to model the spatial-temporal information in the crowd videos.  ...  Images/Videos Condition No.  ... 
arXiv:1811.11849v4 fatcat:2hkn363lxfecjcajiv7smxabym

Latent Variable Algorithms for Multimodal Learning and Sensor Fusion [article]

Lijiang Guo
2019 arXiv   pre-print
We propose a co-learning approach using probabilistic graphical model which imposes a structural prior on the generative model: multimodal variational RNN (MVRNN) model, and derive a variational lower  ...  We study multimodal learning and sensor fusion from a latent variable perspective. We first present a regularized recurrent attention filter for sensor fusion.  ...  Michael Ryoo and Dr. Lantao Liu for helpful discussions.  ... 
arXiv:1904.10450v1 fatcat:6634ghs74fcd3fz3l4nov4rb3m

Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models

Qiang Ji, Jiebo Luo, Dimitris Metaxas, Antonio Torralba, Thomas S. Huang, Erik B. Sudderth
2009 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Third, we would are thankful for the support we have received from the TPAMI editorial office, including past Editor-in-Chief Professor David Kriegman, current Editor-in-Chief Professor Ramin Zabih, and  ...  In particular, we thank Elaine for her timely answers to our inquiries, and for actively keeping the special section on schedule. Qiang Ji Jiebo Luo Dimitris Metaxas Antonio Torralba Thomas S.  ...  Alternatively, undirected graphical models such as Markov random fields (MRFs) and conditional random fields (CRFs) are widely used to model spatial dependencies.  ... 
doi:10.1109/tpami.2009.160 pmid:19757542 fatcat:xdy2dvqolferbeqib7guqdyfyi

MMM: Multi-source Multi-net Micro-video Recommendation with Clustered Hidden Item Representation Learning

Jingwei Ma, Jiahui Wen, Mingyang Zhong, Weitong Chen, Xue Li
2019 Data Science and Engineering  
Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to  ...  In this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users' best interests.  ...  We can observe the hidden categorical structure among the items; namely, intra-cluster items are close to each other, while inter-cluster items are far away from each other in the projected latent space  ... 
doi:10.1007/s41019-019-00101-4 fatcat:cjn4saysczhtxh4fr4tft27gsq

An Online Algorithm for Constrained Face Clustering in Videos

Prakhar Kulshreshtha, Tanaya Guha
2018 2018 25th IEEE International Conference on Image Processing (ICIP)  
We address the problem of face clustering in long, real world videos.  ...  This is a challenging task because faces in such videos exhibit wide variability in scale, pose, illumination, expressions, and may also be partially occluded.  ...  Such constraints were used in a hidden Markov random field-based framework (HMRF) [2] to perform face clustering in videos from movies and TV series.  ... 
doi:10.1109/icip.2018.8451343 dblp:conf/icip/KulshreshthaG18 fatcat:ra3ojjswujcgvpl4vjnxzbbtai

Human Identification using Gait and Face

Rama Chellappa, Amit K. Roy-Chowdhury, Amit Kale
2007 2007 IEEE Conference on Computer Vision and Pattern Recognition  
A time series state space model is proposed to fuse temporal information in a probe video, which simultaneously characterizes the kinematics and identity using a motion vector and an identity variable,  ...  Rather than resolving these two uncertainties separately, our strategy is to perform simultaneous tracking and recognition of human faces from a video sequence.  ... 
doi:10.1109/cvpr.2007.383523 dblp:conf/cvpr/ChellappaCK07 fatcat:66igb4huk5gu5gvwz4lc5c7xmy

Neural mechanisms underlying the temporal organization of naturalistic animal behavior [article]

Luca Mazzucato
2022 arXiv   pre-print
In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior.  ...  Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, whose variability stems from at least three sources: hierarchical, contextual, and stochastic.  ...  Mazzucato and Murray labs for many discussions and suggestions.  ... 
arXiv:2203.02151v1 fatcat:m3cnresdcndz7hhr2qizhrmxlu
« Previous Showing results 1 — 15 out of 8,255 results